This document discusses knowledge representation in artificial intelligence. It begins by discussing what AI is and some of the underlying assumptions of AI techniques. It then discusses how knowledge representation captures generalizations, is understood by people, can be modified, and is useful in different situations. It provides examples of knowledge representation using tic-tac-toe and magic squares. It also discusses representing facts, reasoning, the frame problem, predicate logic, and approaches to knowledge representation.

1. What is AI ?????????????
• The AI problems (eg.Chess etc.)
• The underlying assumption
• What is AI technique ?
- Intelligence requires knowledge
Knowledge properties:-
.. It is voluminous
.. It is hard to characterize accurately
.. It is constantly changing
.. Organized way & usage -differ
AI Technique representation
• knowledge captures generalization
• To be Understood by people who provide it
• Easily to be modified to correct errors
• To be useful in great many situations
• To be useful under narrow range of possibilities that must be considered
Tic- tac – toe
• To play Tic-Tac-toe
• (magic square )
• Complexity
• Generalized usage
• Clarity of their knowledge
• Extensibility of their approach

2. Programs
• Board
• Nine element vector representing the board positions such that no number is to be
repeated
• Row sum = column sum = diagonal sum
• Algorithm
• Comments
Knowledge representation
Facts….> internal ……………..Reasoning
Representation.. programs
English ……………………………….english
Understanding………………..generation
English
Representation
Mapping between facts & representations
· Knowledge level
· Symbol level
English : spot is a dog
Logic : dog(spot)
For every x : dog(x) ..> hastail(x)
Then hastail(spot)

3. Representation of Facts
Initial …..desired real …..final
Facts reasoning facts
! ^
* ! ! *
V !
Forward Backward
representation representation
mapping mapping
operation
of programs
Internal ….………………..> Internal
Representation representation
Of initial of final
Facts facts
Approaches to knowledge representation
· Representation adequacy
· Inferential adequacy
· Inferential efficiency
· Acquisitional efficiency

4. Knowledge
1.Inheritable
2. inferential
3. Procedural
Simple relational knowledge
Player ht wt bats/throws
Hank 6-0 180 right/right
Wille 5-10 176 right/left
Babe 6-2 215 left/left
Wilman 6-3 205 left/right
Representing sets of objects
Eg. Set of SUN’s planets on which people live is { earth}
{ x : sun-planet(x) ^ human-inhabited(x) }
Finding the Right Structures as Needed
· How to perform an initial selection of the most appropriate structures ?
· How to fill in appropriate details from the current situation ?
· How to find a better structure?
· What to do if none is available ?
· When to create and remember a new structure ?

5. The Frame problem
Similarity net……
Chair->Table->Desk->Sideboard
Chair -> Table :: too big, no back
Table-> desk :: drawers
Desk-> sideboard :: no knee room
Using predicate logic
Simple facts in prepositional logic
It is raining
RAINING
It is sunny
SUNNY
It is windy
WINDY
If it is raining , then it is not sunny
RAINING  NOT SUNNY
Eg. Socrates is a man
Plato is a man
Then represent as follows :-
MAN(SOCRATES)
MAN(PLATO)

6. Some Facts
1.Marcus was a man.
2. Marcus was Pompeian.
3. All Pompeians were Romans.
4. Caesar was a ruler.
5. All Romans were either loyal to Caesar or hated him.
6. Everyone is loyal to someone.
7. People only to try assassinate rulers they are not loyal to.
8. Marcus tried to assassinate Caesar .
Now write the Predicate logic :-
1. man(Marcus)
2. Pompeian(Marcus)
3. for all x such that Pompeian(x) -> Roman(x)
4. ruler(Caser)
5. for all x such that Roman(x)
->loyalto(x,Caesar) or hate(x,Caesar)
6. for all x, there exists y such that loyalto(x,y)
7. for all x such that for all y person(x) and ruler(y) and tryassassinate(x,y) ->not
loyalto(x,y)
8. tryassassinate(Marcus,Casear)

7. Conclusion:-
Was Marcus loyal to Caesar ?
From 7 & 8 we prove that Marcus was not royal to Caesar.
Notroyal(marcus,Caesar)
Symbolic Reasoning under uncertainty
Introduction to Nonmonotonic Reasoning
· To reason with incomplete information
· At any time , a statement is believed to be true, believed to be false, or not believed to be
either
Whereas in Statistical reasoning, the representation allows some kind of numerical measure of
certainty ( rather than simple true or false )
Eg. Consider a case :
1.That Abbott did not commit the crime
2. That Babbitt did not
3. That Abbott or Babbitt or Cabot did
4. That Cabot did not
Properties of conventional reasoning system
· It is complete with respect to the domain of interest
· It is consistent
· The facts can be added when they are available

8. If these properties are missing then conventional logic based reasoning systems will not work.
· At that time Nonmonotonic reasoning systems are designed to solve the problems.
Logics for Nonmonotonic reasoning
1.Define the set of possible words for the given facts
2. Provide a way that we believe in some models rather than others
3. Provide the basis for the practical implementation of this kind of reasoning
4. Correspond to our intuitions about how this kind of reasoning works
To use nonmonotonic reasoning, Default reasoning is necessary
Default reasoning :
To draw conclusions based on what most likely to be true
Nonmonotonic Logic(NML)
Consider the formula
For all x,y : Related(x,y) and
M Getalong(x,y) -> WillDefend(x,y)
This means that
For all x,y , if x and y are related and if x gets along with y with everything else that is believed ,
then conclude that x will defend y
Here M is called modal operator
In the original formulation of NML , the semantics of the modal operator M which is self-
referential , were unclear.

9. Default Logic(DL)
Reiter’s Default logic has the inference rule has
A : B
C
This means that
“ if A is provable and it is consistent to assume B then conclude C “
Abduction
Standard logic performs deduction
Given two axioms,
For all x, : A(x) -> B(x)
A( C )
Then we can conclude B(C) using deduction.
What about reverse ?
For example
Suppose the axiom as
For all x : Measles(x)->Spots(x)
This means that Measles implies having spots.
Supposing we notice spots, we conclude Measles on the reverse.
But that conclusion is not licensed by the standard rule of logic and it may be wrong too many a
times.
But it may be a best Guess !
This specific form is called abductive reasoning.
Inheritance

10. One common use of nonmonotonic reasoning is as a basis for inheriting attribute values from a
prototype description of a class to the individual entities that belong to the class.
Minimalist reasoning
Methods for saying very specific and highly useful class of things that are generally true.
Closed world assumption (CWA)
A simple kind of minimalist reasoning is suggested by CWA
Eg. A(Joe) or B(Joe)
This is a single statement
The resulting extended knowledge base is
A(Joe) or B(joe)
Not A(Joe)
Not B(joe)
Is inconsistent.
Again consider
Single(John)
Single(Mary)
The CWA will yield
Not single(jane)
Now consider the predicate married in place of single.
Not Married(John)
Not Married(Mary)
The CWA will yield
Not Married(Jane)
Implementation issues

11. In real systems follow the points:-
1.How to derive exactly those nonmonotonic conclusions that are relevant to solve the
problems.
2. How to increment our knowledge base as problem progresses.
3. In nonmonotonic reasoning systems, more than one interpretation is possible .
How to manage these ?
4. These theories are not computationally effective and so none is effective.
Augmenting a problem solver
1.Reason forward from what is known
2. Reason backward to find out whether some expression P is true.
Truth maintenance systems
Justification based truth maintenance systems
Logic based truth maintenance systems
Statistical reasoning
Probability
· Tossing a coin and getting head or tail
· P(H)= 1/2 P(T)=1/2
· Rolling a die
· P(any No.to occur)=1/6
· P(Hi / E) = Probability that hypothesis Hi is true given evidence E
· P(E / Hi) = the probability that we will observe evidence E given that hypothesis
Hi is true

12. · P(Hi) = the a priori probability that hypothesis Hi is true in the absence of any
specific evidence.
· K= No of possible hypothesis
· Then Baye’s theorem states that
· P(Hi / E) = P(E/Hi) . P(Hi)
· ----------------
· sum(P(E/Hi).P(Hi))
· PROSPECTOR is AI system to locate any mineral from the earth using the above
theorem (1979 Duda et.al.)
Example : Ball problems
There are two boxes A & B
A has 4 Red balls 3 White balls
B has 5 Red balls 4 White balls
Q1. What is P(A/R) ?
Q2. What is P(A/W) ?
Q3. What is P(B/R) ?
Q4. What is P(B/W) ?
nextQ.A red ball from A is put in B
Q1. What is P(A/R) ?
Q2. What is P(A/W) ?
Q3. What is P(B/R) ?
Q4. What is P(B/W) ?
nextQ.A white ball of A is put in B
Q1. What is P(A/R) ?

13. Q2. What is P(A/W) ?
Q3. What is P(B/R) ?
Q4. What is P(B/W) ?
Similarly reverse problem can be thought of.
Q.There are two boxes A & B
A has 5 Red balls 3 White balls
B has 3 Red balls 5 White balls
A ball of A is put in B
Then What is P(A/R) ?
What is P(A/W) ?
After transferring the ball,
It can be either R or W.
Mutually exclusive case.
What is P(B/R) ?
What is P(B/W) ?
Thus, Baye’s theorem helps this type of problem-solving as a statistical reasoning.
Applied intelligent system in oil & gas industry
Case study
There are 5 wild cat areas of oil & gas as follows:-
Area 1 : 3 gas and 2 oil wells
Area 2 : 1 gas and 2 oil wells

14. Area 3 : 4 gas and 2 oil wells
Area 4 : 2 gas and 4 oil wells
Area 5 : 5 gas and 3 oil wells
Apply Baye’s theorem and discuss
The following :-
P(G) ? P(O) ?
P(G OR O) ?
P(G AND O) ?
Certainty Factors & Rule-based systems
MYCIN system
It is a kind of rule based expert system.
- performs the task normally done by human expert 1
- It represents most of its diagnostic knowledge as a set of rules.
A typical rule
If 1. the strain of the organism is gram-positive and
2.the morphology of the organism is coccus and
3. the growth confirmation of the organism is clumps,
then there is suggestive evidence that the identity of the organism is staphylococcus.
This is the form in which the rules are stated to the user.
Uncertain Rules
Now combine uncertain rules and apply the properties :-
1. Since the order in which evidence is collected is arbitrary, the combining functions should be
commutative and associative.

15. 2. Until certainty is reached , additional combining evidence should increase MB and (similarly
for disconfirming evidence and MD) where MB means a measure of belief ( between 0 and 1)
and MD is a measure of disbelief (between 0 and 1)
3. If uncertain influences are chained together then the result should be less certain than either of
the inferences alone.
Game playing
Games provide good domain to explore machine intelligence :
1. Providing a structured task in which it is easy to measure success or failure
2. They need not large amount of knowledge.
The first is true but not the second !
Why ?
Consider chess game. An average player needs minimum of 50 moves ! so in order to complete
the game we have to examine 100 to the power of 35, if average branching factor is 35 !
Thus huge amount of knowledge is necessary.
The MINIMAX search
One ply-search
A
B C D
8 3 -2
Apply Minimum ply
Then A(-2)
Apply maximum ply
Then A(8)
Two ply search

16. A
B C D
E F G H I J K
9 -6 0 0 -2 -4 -3
APPLY MINIMIZING AND MAXIMIZING AND GET THE BACK-UP VALUES
Apply Minimizing ply ,
We find that
B(-6) , C(-2), D(-4)
Then apply Maximizing ply,
We get A(-2)
Application :
In the case of profit center concept, maximizing ply and in the case of expenses, minimizing ply
are applied as applied intelligent systems.
This is called Minimax Search Procedure.
· This procedure is very simple
· Performance is improved significantly with a few refinements.
How ?
Consider the example :
Alpha Cutoff

17. A Maximize
B C
D E F G Minimize
3 5 -5
Answer
A(>3) Maximize
B(3) C(< -5)
D E F G Minimize
3 5 -5
Similarly when we apply Minimizing ply, then maximizing ply, then minimizing ply, and then
maximizing ply, it is called Alpha and Beta Cutoffs.
There are other varieties of modifications to minimax procedure to improve its performance.
Class work :
Create some example for Alpha and Beta procedure .
Natural language processing
Language – for communication about the world.
Language processing – two taks
1. Processing written text, using lexical , syntactic and semantic knowledge of the
language
2. Processing spoken language, using all the information needed + additional
knowledge to handle any confusions that arise in speech.

18. Problem
Some dogs are outside =>
1.Some dogs are on the lawn
2.Three dogs are on the lawn
3.Rover, Tripp, Spot are on the lawn.
I called Lynda to ask her to the movies. She said she would love to go. =>
1. She was home when I called.
2. She answered the phone.
3. I actually asked her.
Good side
1. Language allows speakers to be as vague or as precise as they like.
2. It also allows speakers to leave out things they believe their hearers already know.
Problem
The same expression means different things in different context.
1. Where’s the water ?(In chemistry lab, it must be pure)
2. Where’s the water ? (when you are thirsty ,it must be potable)
3. Where’s the water ? (dealing with a leaky roof )
Good side
Language lets us communicate about an infinite world using a finite number of symbols.
The problem (lot of ways of saying)
Mary was born on Oct.11.

19. Mary’s birthday falls on Oct.11.
Good side:
when you know a lot ,facts imply each other.
Thus language is used as agents who know a lot.
Steps in the process
1. Morphological Analysis
- Individual words are analysed into components.
- Punctuations are separated from words.
‘print’,’file’ can all function more than one syntactic category.
2. Syntactic Analysis
Linear sequences of words are transformed into structures that show how the words relate to
each other.
Some word sequences may be rejected if they violate the rules of the language grammer.
Eg. “Boy the go to store”
3. Semantic Analysis
The structure created by the syntactic analyzer are assigned meanings. In other words, a mapping
is made between syntactic structures and objects in the task domain. If no such mapping is
possible then such structures will be rejected.
Eg. “colorless green ideas sleep furiously” will be rejected.
A knowledge base fragment
User068
Instance: user

20. Login-name: susan-black
F1
Instance: File-struct
Name: stuff
Extension: .init
Owner: user073
In-director: /wsmith/
4. Discourse Integration
The meaning of individual sentence may depend on the sentences that precede it and may
influence the meaning of sentences that follow . For eg. ‘it’,
‘John wanted it ‘
5. Pragmatic Analysis
The structure representing what was said is reinterpreted to find out what was actually meant.
Eg. ‘ Do you know what time is ?’ is reinterpreted as a request to be told time.
Representing the indended meaning
Meaning
Instance: commanding
Agent: user068
Performer: this-system
Object: p27
Instance: printing
Agent: this-system
Object: F1

21. Expert Systems (ES)
Expert system is a prototype computerized system to behave like an expert in a particular filed of
specialization, like medical, oil & gas industry etc.
Example :
1. MYCIN is an expert system in medical field.
2. PROSPECTOR is an expert system in Oil & Gas industry.
Expert system is classified as
ES = KA + KB + IA + PD
WHERE
ES :: Expert system
KA :: Knowledge Acquisition
KB :: Knowledge Base
IA :: Inferential Analysis
PD :: Prototype Development
Discuss : Medical system under above classification.
Merits of Expert System :
· Acts like an expert when real expert in a particular filed of specialization is not
available.
· Time saving.
· Cost – effective.
· Most Modern.
· Faster solution provider.
· No need of appointment of real expert.
· Multiple solutions are possible.

22. · Multiple consultations are possible.
· Case – study oriented.
· Easy to use.
Demerits
· Successful 25-30 % only world wide.
· Real Experts do not offer their expertise knowledge to develop the system for fear
of loosing importance personally.
· If not updated periodically, wrong conclusions are possible.
· Due to multiple solutions, some times confusions are possible.
· Awareness in the minds of people is lacking and thereby belief is not there much.
· A lot of difficulty in Knowledge Acquisition.
· Time consuming job – development of ES.
IMPORTANT:
Entering knowledge , maintaining the knowledge base consistency , ensuring knowledge
base completeness are the key factors of success of expert systems.
MOLE (Eshelman 1988) is a knowledge acquisition system dealing with diagnosing
diseases.
SALT (Marcus 1989) is a program to provide mechanism for extracting knowledge from
an expert.
META-DENDRAL (Mitchell 1978) is the first program to use learning techniques to
construct rules for an expert system automatically.
Conclusions:

23. · Expert systems offer their power in a domain specific knowledge rather than a
single powerful technique
· In successful systems, the needed knowledge is about a particular area and is well-
defined.
· An expert system is built normally with the aid of one or more experts.
· Transfer of knowledge takes place gradually after many interactions.
· The amount of knowledge is depending on a task.
· The choice of control structure depends on specific characteristics of the system.
· It is possible to extract non-domain specific parts from existing expert systems and use
them to build a new expert system in new domains.
What is learning ?
Often AI – machines are blamed that they cannot be called intelligent until they are able to do
new things and adapt to new situations rather than simply doing things as directed !
Knowledge Acquisition itself includes many different activities.
- Simple storing of computed information or rote learning , is the basic learning activity.
Rote learning
A
B C D
E F G H I J K
- Rote learning of this sort is very simple.
- It does not appear to involve any sophisticated problem solving capabilities .

24. - ORGANISED storage information
- generalization
Learning by taking advice
A computer program might make use of the advice by adjusting its static evaluation function to
include factor-based on the number of center squares attacked by its own pieces , in Chess.
Learning in problem solving
- Learning by parameter adjustment
- Learning with macro - operators
- Learning by Chunking : is a process similar to macro-operators
(memory & problem solving )
Learning from examples : Induction
- class discussions
Decision trees
- class discussions
Explanation based learning
- class discussions
Discovery
- class discussions
Formal Learning theory
- class discussions
Neural Nets
First efforts in machine learning – tried to mimic animal learning at a neural level !

25. - This is quite different from symbolic manipulation methods
- Neural network models are based on computational “brain metaphor”
- A number of other learning techniques make use of metaphor based on evolution.
- Learning algorithms inspired by evolution are called genetic algorithms.
Learning in Neural Networks
Perceptrons
- An invention of Rosenblatt(1962), one of the earliest nueral network models.
- A perceptron models a neuron by taking weighted sum of its inputs and sending the output ‘1’
if the sum is greater than some adjustable threshold value or otherwise it send ‘0’.
Let the inputs be (x1,x2,……xn) and the weights be (w1,w2,…..wn)
Consider g(x) = sum(wi.xi)
o(x) = 1 if g(x) > 0
= 0 if g(x) < 0
Early notion of intelligent system built from Trainable perceptrons
A linearly separable pattern classification problem fig. 18.9 page 496.
Applications of neural networks
- Pattern recognizers and associative memories
- Pattern transformers
- Dynamic inferencers
- Most of the people use the first category in industrial applications.
- Some are using the second category too.

26. - Third one is still in primitive stage.
Turbo prolog programming
It is a trade-mark of Borland international Inc.
- The name ‘prolog’ is taken from ‘programming in logic’
- Originally developed in 1972 by
Alain Colmerauer & P.Roussel,
University of Marseiles,France.
Starting Turbo prolog
c>PROLOG
Then the user-friendly editor appears as follows:-
Run Compile Edit Options Files Setup Quit
Editor
Line1 Col1 Indent Insert Work.pro
----Dialog-----
--------Message---------
--------Trace----------

27. Use first letter of Option or select with -> or <-
Create a Sample program
1.Select Edit mode & enter program
2. Enter text or corrections as necessary
3. Save the program to disk
4. Compile the program
5. Execute the program
Similar to Turbo pascal
SAMPLE TEST PROGRAM
Run Compile Edit Options Files Setup Quit
Editor
Line1 Col1 Indent Insert Work.pro
/* SAMPLE TEST PROGRAM */
predicates
likes(symbol , symbol)
clauses
likes(frank,sue).
----Dialog-----

28. likes(Harold,ruth).
--------Message---------
--------Trace----------
F1:Help F3:Search F4:Subst F5:Copy F6:Move F7:del F8:ExtEdit F9:ExtCopy F10:End
Starting to execute the program
To compile and execute the
program exit the editor using Esc
or F10. Then select the Run option
from the main menu.
If NO errors, then see the prompt
in dialog box as follows:-
If error, turbo prolog will return
to the ditor.
Then compile again till ‘No error’

29. SAMPLE TEST PROGRAM
Run Compile Edit Options Files Setup Quit
Editor
Line1 Col1 Indent Insert Work.pro
/* SAMPLE TEST PROGRAM */
predicates
likes(symbol , symbol)
clauses
likes(frank,sue).
likes(Harold,ruth).
----Dialog-----
Goal :-
--------Message---------
--------Trace----------
F1:Help F3:Search F4:Subst F5:Copy F6:Move F7:del F8:ExtEdit F9:ExtCopy F10:End
Goal : likes(Harold,ruth)<-! (return key press )
Turbo Prolog will reply to you as
True
Goal : ( wait for next goal )
Expressing Facts
(English to (Turbo)T. Prolog)
The right speaker is dead (English)
Is(right_speaker,dead). (T.prolog)

30. Fact Relation
has_a(bill, computer). has_a
is_a(collie,dog). is_a
likes(sue,chocolate). Likes
Predicates (examples)
employee(bill)
eligible(mary)
marital_status(joyce,married)
For example, you can express the fact that mary is married and is eligible for employment as
eligible(mary) and marital-status(mary,married).
Facts expressed as prolog clauses
English: Bill is an employee.
Prolog: employee(Bill).
English: Bob is married to Mary.
Prolog: married_to(Bob,Mary).
English:The speaker is defective.
Prolog: defective_speaker.
English: Tom is a student.
Prolog: student(Tom).
Relationship of clauses, predicates, relations and objects.

31. Clauses
|
|__________________|
Facts Rules
|________________|
Predicates Periods(.)
|____________|
Relations Arguments
|
|_______________|
Objects Variables
Example : Not for medical use !
domains
disease,indication=symbol
predicates
symptom(disease,indication)
clauses
symptom(chicken_pox,high_fever). symptom(chicken_pox,chills). symptom(flu,chills).
symptom(cold,mild_body_ache). symptom(flu,severe_body_ache).
symptom(cold,runny_nose). symptom(flu,runny_nose). symptom(flu,moderate_cough).
Goal : symptom(cold,runny_nose) <-!
Response from prolog :
True

32. Goal : symptom(cold,headache) <-!
Response from prolog :
False
Using rules to solve the problems
Here another example to use
rules to solve the problems,
using hypothesis- concepts
Example : Medical Diagnostic System
domains
disease,indication,name=symbol
predicates
hypothesis(name,disease)
symptom(disease,indication)
clauses
symptom(charlie,fever). symptom(charlie,rash). symptom(charlie,headache).
symptom(charlie,runny_nose).
Hypotheis(patient,measles):-
symptom(charlie,fever), symptom(charlie,rash), symptom(charlie,headache),
symptom(charlie,runny_nose),
symptom(charlie,cough).
Hypotheis(patient,german_measles):-
symptom(charlie,fever), symptom(charlie,rash), symptom(charlie,headache),
symptom(charlie,runny_nose).
Hypotheis(patient,flu):-

33. symptom(charlie,fever), symptom(charlie,headache), symptom(charlie,body_ache),
symptom(charlie,runny_nose),
symptom(charlie,cough).
Hypotheis(patient,mumps):-
symptom(charlie,fever), symptom(charlie,swollen_glands).
Goal : hypothesis(patient,disease)
Prolog display as follows:-
Goal : hypothesis(patient,disease)
Patient=Charlie,disease=german_measles
-O-